Dataset Open Access
Fiedler, Lenz;
Shah, Karan;
Cangi, Attila;
Bussmann, Michael
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<identifier identifierType="DOI">10.14278/rodare.1197</identifier>
<creators>
<creator>
<creatorName>Fiedler, Lenz</creatorName>
<givenName>Lenz</givenName>
<familyName>Fiedler</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8311-0613</nameIdentifier>
<affiliation>HZDR / CASUS</affiliation>
</creator>
<creator>
<creatorName>Shah, Karan</creatorName>
<givenName>Karan</givenName>
<familyName>Shah</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5480-2880</nameIdentifier>
<affiliation>HZDR / CASUS</affiliation>
</creator>
<creator>
<creatorName>Cangi, Attila</creatorName>
<givenName>Attila</givenName>
<familyName>Cangi</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9162-262X</nameIdentifier>
<affiliation>HZDR / CASUS</affiliation>
</creator>
<creator>
<creatorName>Bussmann, Michael</creatorName>
<givenName>Michael</givenName>
<familyName>Bussmann</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8258-3881</nameIdentifier>
<affiliation>HZDR / CASUS</affiliation>
</creator>
</creators>
<titles>
<title>Dataset and scripts for A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry</title>
</titles>
<publisher>Rodare</publisher>
<publicationYear>2021</publicationYear>
<dates>
<date dateType="Issued">2021-10-01</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Dataset"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/1197</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-33194</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.1196</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
</relatedIdentifiers>
<version>v1.0.0</version>
<rightsList>
<rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract"><p>This dataset contains additional data for the publication &quot;A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry&quot;. Its goal is to enable interested people to reproduce the citation analysis carried out in the aforementioned publication. &nbsp;</p>
<p>&nbsp;</p>
<p><strong>Prerequesites</strong></p>
<p>The following software versions were used for the python version of this dataset:</p>
<p>Python: 3.8.6</p>
<p>Scholarly: 1.2.0</p>
<p>Pyzotero: 1.4.24</p>
<p>Numpy: 1.20.1</p>
<p>&nbsp;</p>
<p><strong>Contents</strong></p>
<p>results/ : Contains the .csv files that were the results of the citation analysis.&nbsp; Paper groupings follow the ones outlined in the publication.</p>
<p>scripts/ : Contains scripts to perform the citation analysis.</p>
<p>Zotero.cached.pkl : Contains the cached Zotero library.</p>
<p>&nbsp;</p>
<p><strong>Usage</strong></p>
<p>In order to reproduce the results of the citation analysis, you can use citation_analysis.py in conjunction with cached Zotero library. Manual additions can be verified using the check_consistency script.<br>
Please note that you will need a Tor key for the citation analysis, and access to our Zotero library if you don&#39;t want to use the cached version. If you need this access, simply contact us.</p>
<p>&nbsp;</p></description>
</descriptions>
</resource>
| All versions | This version | |
|---|---|---|
| Views | 2,174 | 1,107 |
| Downloads | 510 | 274 |
| Data volume | 630.0 MB | 299.9 MB |
| Unique views | 1,148 | 642 |
| Unique downloads | 108 | 70 |